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A Comparative Study of Adaptive Filtering Strategies for Hybrid Energy Storage Systems in Electric Vehicles

Author

Listed:
  • Hoai-Linh T. Nguyen

    (CTI Laboratory for EVs, School of Electrical Engineering, Hanoi University of Science and Technology, Hanoi 10000, Vietnam)

  • Bảo-Huy Nguyễn

    (CTI Laboratory for EVs, School of Electrical Engineering, Hanoi University of Science and Technology, Hanoi 10000, Vietnam
    e-TESC Laboratory, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada)

  • Thanh Vo-Duy

    (CTI Laboratory for EVs, School of Electrical Engineering, Hanoi University of Science and Technology, Hanoi 10000, Vietnam)

  • João Pedro F. Trovão

    (e-TESC Laboratory, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
    INESC Coimbra, DEEC, University of Coimbra, Polo II, 3030-290 Coimbra, Portugal
    Polytechnic Institute of Coimbra, IPC-ISEC, DEE, 3030-199 Coimbra, Portugal)

Abstract

Hybrid energy storage systems (HESSs) including batteries and supercapacitors (SCs) are a trendy research topic in the electric vehicle (EV) context with the expectation of optimizing the vehicle performance and battery lifespan. Active and semi-active HESSs need to be managed by energy management strategies (EMSs), which should be realized on real-time onboard platforms. A widely used approach is the filter-based EMS thanks to its simplicity and effectiveness. However, one question that always arises with these algorithms is how to determine the appropriate constant cut-off frequency. To tackle this challenge, this paper proposed three adaptive schemes for the filtering strategies based on the SC “ability” and evaluated their performance during the vehicle operation via an intensive comparative study. Offline simulation and experimental validation using signal hardware-in-the-loop (HIL) emulation showed that the proposed adaptive filtering EMS can reduce the battery rms current considerably. Specifically, the SC-energy-based, SOC-based, and voltage-based algorithms minimized the battery rms by up to 69%, 66%, and 64%, respectively, when compared to a pure battery EV in a fluctuating driving condition such as the urban Artemis cycle.

Suggested Citation

  • Hoai-Linh T. Nguyen & Bảo-Huy Nguyễn & Thanh Vo-Duy & João Pedro F. Trovão, 2021. "A Comparative Study of Adaptive Filtering Strategies for Hybrid Energy Storage Systems in Electric Vehicles," Energies, MDPI, vol. 14(12), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3373-:d:571106
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    References listed on IDEAS

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    Cited by:

    1. Zhangyu Lu & Xizheng Zhang, 2022. "Composite Non-Linear Control of Hybrid Energy-Storage System in Electric Vehicle," Energies, MDPI, vol. 15(4), pages 1-15, February.
    2. Vishnu P. Sidharthan & Yashwant Kashyap & Panagiotis Kosmopoulos, 2023. "Adaptive-Energy-Sharing-Based Energy Management Strategy of Hybrid Sources in Electric Vehicles," Energies, MDPI, vol. 16(3), pages 1-26, January.
    3. Chi T. P. Nguyen & Bảo-Huy Nguyễn & Minh C. Ta & João Pedro F. Trovão, 2023. "Dual-Motor Dual-Source High Performance EV: A Comprehensive Review," Energies, MDPI, vol. 16(20), pages 1-28, October.

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